Journal of Integrative Bioinformatics最新文献

{"title":"ExceS-A: an exon-centric split aligner","authors":"Franziska Reinhardt, P. Stadler","doi":"10.1515/jib-2021-0040","DOIUrl":"https://doi.org/10.1515/jib-2021-0040","url":null,"abstract":"Abstract Spliced alignments are a key step in the construction of high-quality homology-based annotations of protein sequences. The exon/intron structure, which is computed as part of spliced alignment procedures, often conveys important information for the distinguishing paralogous members of gene families. Here we present an exon-centric pipeline for spliced alignment that is intended in particular for applications that involve exon-by-exon comparisons of coding sequences. We show that the simple, blat-based approach has advantages over established tools in particular for genes with very large introns and applications to fragmented genome assemblies.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48295319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic study of motif pairs that may facilitate enhancer-promoter interactions.","authors":"Saidi Wang,&nbsp;Haiyan Hu,&nbsp;Xiaoman Li","doi":"10.1515/jib-2021-0038","DOIUrl":"https://doi.org/10.1515/jib-2021-0038","url":null,"abstract":"<p><p>Pairs of interacting transcription factors (TFs) have previously been shown to bind to enhancers and promoters and contribute to their physical interactions. However, to date, we have limited knowledge about such TF pairs. To fill this void, we systematically studied the co-occurrence of TF-binding motifs in interacting enhancer-promoter (EP) pairs in seven human cell lines. We discovered 423 motif pairs that significantly co-occur in enhancers and promoters of interacting EP pairs. We demonstrated that these motif pairs are biologically meaningful and significantly enriched with motif pairs of known interacting TF pairs. We also showed that the identified motif pairs facilitated the discovery of the interacting EP pairs. The developed pipeline, EPmotifPair, together with the predicted motifs and motif pairs, is available at https://doi.org/10.6084/m9.figshare.14192000. Our study provides a comprehensive list of motif pairs that may contribute to EP physical interactions, which facilitate generating meaningful hypotheses for experimental validation.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39897140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCARF: a biomedical association rule finding webserver.","authors":"Balázs Szalkai,&nbsp;Vince Grolmusz","doi":"10.1515/jib-2021-0035","DOIUrl":"https://doi.org/10.1515/jib-2021-0035","url":null,"abstract":"<p><p>The analysis of enormous datasets with missing data entries is a standard task in biological and medical data processing. Large-scale, multi-institution clinical studies are the typical examples of such datasets. These sets make possible the search for multi-parametric relations since from the plenty of the data one is likely to find a satisfying number of subjects with the required parameter ensembles. Specifically, finding combinatorial biomarkers for some given condition also needs a very large dataset to analyze. For fast and automatic multi-parametric relation discovery association-rule finding tools are used for more than two decades in the data-mining community. Here we present the SCARF webserver for <i>generalized</i> association rule mining. Association rules are of the form: <i>a</i> AND <i>b</i> AND … AND <i>x</i> → <i>y</i>, meaning that the presence of properties <i>a</i> AND <i>b</i> AND … AND <i>x</i> implies property <i>y</i>; our algorithm finds generalized association rules, since it also finds logical disjunctions (i.e., ORs) at the left-hand side, allowing the discovery of more complex rules in a more compressed form in the database. This feature also helps reducing the typically very large result-tables of such studies, since allowing ORs in the left-hand side of a single rule could include dozens of classical rules. The capabilities of the SCARF algorithm were demonstrated in mining the Alzheimer's database of the Coalition Against Major Diseases (CAMD) in our recent publication (Archives of Gerontology and Geriatrics Vol. 73, pp. 300-307, 2017). Here we describe the webserver implementation of the algorithm.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39888837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative Bioinformatics: History and Future","authors":"","doi":"10.1007/978-981-16-6795-4","DOIUrl":"https://doi.org/10.1007/978-981-16-6795-4","url":null,"abstract":"","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"78 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83634855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data.","authors":"Arthur I Dergilev, Nina G Orlova, Oxana B Dobrovolskaya, Yuriy L Orlov","doi":"10.1515/jib-2020-0036","DOIUrl":"10.1515/jib-2020-0036","url":null,"abstract":"<p><p>The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in <i>Arabidopsis thaliana</i> genome were discussed here in detail.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39761184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue of the 1st International Applied Bioinformatics Conference (iABC'21).","authors":"Jens Allmer,&nbsp;Mourad Elloumi,&nbsp;Matteo Comin,&nbsp;Ralf Hofestädt","doi":"10.1515/jib-2021-0042","DOIUrl":"https://doi.org/10.1515/jib-2021-0042","url":null,"abstract":"Diseases can be tied to changes at the molecular level within affected cells. This can be concerning transcription, translation, or any other mechanism involved in gene expression, such as post-transcriptional regulation. Instrumentation for the measurement of such molecular changes is readily available and produces large amounts of data. For example, DNA and RNA sequencing, as well as protein quantitation, and sequencing can be achieved via next-generation sequencing andmass spectrometry, respectively. One current challenge is the analysis and integration of the resulting heterogeneous and large datasets. Bioinformatics is the field of study which produces algorithms and integrative approaches to attempt suchdata analyses. The primary aim in algorithmic bioinformatics is, however, the development of algorithms and not their application. Typically, novel algorithms are introduced with a proof of principle, and they are applied to some data for that purpose, but usually not comprehensively. Their data might slightly differ from the proof of principle, inducing further data analysis challenges. Additionally, applying such algorithms to their data may be involved for researchers from the biomedical domain. The 1st International Applied Bioinformatics Conference was conceived to bring together representatives from all research fields involved to increase knowledge transfer. First planned for 2020 and then deferred to 2021 due to the pandemic caused by the Coronavirus [1], the conference was held online. Despite the virtual nature of the conference, attentionwas great.We receivedmany goodmanuscripts and invited a few to submit their full versions to this special issue. The range of topics was extensive, but many submissions concerned the interface of bioinformatics and its application. The selected papers for this special issue also discuss various topics such as sequence alignment and gene network reconstruction. The first paper in this special issue concerns a challenging issue in bioinformatics, the usage of pangenomes instead of single reference genomes and offers a fast variation-aware read mapping algorithm [2]. Mapping is also vital to investigate gene expression, which is essential for the secondmanuscript. It discusses how microRNA and mRNA expression profiles can be investigated [3]. From this, modular networks are inferred, describing post-transcriptional regulatory networks. Such networks are challenging to visualize, which is the focus of the third paper [4]. The work summarizes the state-of-the-art in bicluster visualization and is also based on gene expression data. Next, we move from transcriptomics to metabolomics. A disparity filter was applied to perform network analysis for colorectal cancer as a proof of principle [5]. The final two manuscripts focus more on practical application in cancer. First, the prostate, ovary, testes, and embryo","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"18 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39729371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the possible effect of miR-203a-3p and miR-29a-3p on <i>DNMT3B</i> and <i>GAS7</i> genes expression.","authors":"Afgar Ali,&nbsp;Sattarzadeh Bardsiri Mahla,&nbsp;Vahidi Reza,&nbsp;Farsinejad Alireza","doi":"10.1515/jib-2021-0016","DOIUrl":"https://doi.org/10.1515/jib-2021-0016","url":null,"abstract":"<p><p>Aberrant expression of genes involved in methylation, including DNA methyltransferase 3 Beta (<i>DNMT3B</i>), can cause hypermethylation of various tumor suppressor genes. In this regard, various molecular factors such as microRNAs can play a critical role in regulating these methyltransferase enzymes and eventually downstream genes such as growth arrest specific 7 (<i>GAS7</i>). Accordingly, in the present study we aimed to predict regulatory effect of miRNAs on <i>DNMT3B</i> and <i>GAS7</i> genes expression in melanoma cell line. hsa-miR-203a-3p and hsa-miR-29a-3p were predicted and selected using bioinformatics software. The Real-time PCR technique was performed to investigate the regulatory effect of these molecules on the <i>DNMT3B</i> and <i>GAS7</i> genes expression. Expression analysis of <i>DNMT3B</i> gene in A375 cell line showed that there was a significant increase compared to control (<i>p</i> value = 0.0015). Analysis of hsa-miR-203a-3p and hsa-miR-29a-3p indicated the insignificant decreased expression in melanoma cell line compared to control (<i>p</i> value < 0.05). Compared to control, the expression of GAS7 gene in melanoma cells showed a significant decrease (<i>p</i> value = 0.0323). Finally, our findings showed that the decreased expression of hsa-miR-203a-3p and hsa-miR-29a-3p can hypothesize that their aberrant expression caused <i>DNMT3B</i> dysfunction, possible methylation of the <i>GAS7</i> gene, and ultimately decreased its expression. However, complementary studies are necessary to definite comment.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39731426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis.","authors":"Nisar Wani,&nbsp;Debmalya Barh,&nbsp;Khalid Raza","doi":"10.1515/jib-2021-0029","DOIUrl":"https://doi.org/10.1515/jib-2021-0029","url":null,"abstract":"<p><p>Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"18 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39640284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics.","authors":"Silvia Sabatini,&nbsp;Amalia Gastaldelli","doi":"10.1515/jib-2021-0030","DOIUrl":"https://doi.org/10.1515/jib-2021-0030","url":null,"abstract":"<p><p>Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"18 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39635704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In silico</i> approach to understand epigenetics of POTEE in ovarian cancer.","authors":"Sahar Qazi,&nbsp;Khalid Raza","doi":"10.1515/jib-2021-0028","DOIUrl":"https://doi.org/10.1515/jib-2021-0028","url":null,"abstract":"<p><p>Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"18 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39631904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}